International Journal of Electrical and Computer Engineering (IJECE)
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Internet of things heatstroke detection device
Get PDFThe increasing frequency and intensity of heat waves due to climate change underscore the critical need for proactive measures to prevent heat stroke, a life-threatening condition affecting individuals of all demographics, with vulnerability among the elderly and outdoor workers. In response to this pressing public health challenge, we present the internet of things (IoT) based heat stroke prevention device, a comprehensive solution leveraging a suite of sensors including temperature, atmospheric, pulse rate, blood pressure, and gyroscope sensors, seamlessly integrated with an ESP32 microcontroller and Firebase's real-time database. Central to the device's functionality is a random forest classifier machine learning model, trained on historical data and user-specific parameters, to accurately predict the likelihood of heat stroke onset in real-time. Rigorous testing and validation procedures demonstrate the device's high accuracy and reliability in sensor measurements, data transmission, and model performance. The accompanying web-based dashboard provides users with intuitive access to their current health metrics, including temperature, humidity, blood pressure, pulse rate, and personalized predictions for heat stroke risk. This innovative device serves as a versatile tool for public health agencies, occupational safety programs, and individuals seeking to safeguard their well-being in the face of escalating temperatures and climate uncertainties
Hybrid neurocontrol of irrigation of field agricultural crops
Get PDFThis study investigates a conceptual framework for a hybrid intelligent control system designed to optimize the irrigation practice for field crops via fertigation technologies. This research is aimed at enhancing irrigation management through the improvement of the prediction, optimization, and regulation processes. This is achieved through the incorporation of modern computational intelligence with advanced deep learning based neural networks, evolutionary optimization algorithms, and the adaptive neuro-fuzzy technique. This hybrid control framework is made up of interconnected sets of monitoring and decision-making modules. These include subsystems for evaluation of soil conditions, monitoring of plant growth and physiological development, assessment of environmental and climatic conditions, and measurements of the intensity of solar radiation. Additional systems address the preparation of the fertigation mixture and control of intelligent decision-making processes. For this system, the overall control policy is rendered through a predictive neurocontrol approach with execution on a computer platform. A recurrent deep neural model, long short-term memory (LSTM) type, provides crop growth and development parameter predictions through the ability to explore temporal dependencies in agricultural processes. Optimization in the predictive control feedback is accomplished through genetic algorithms in an adaptive manner
Methods for identifying informative features in agricultural images
Get PDFThe paper deals with informative aspects of images, their scope and extraction methods. The research addresses numerous different types of features such as texture, color, geometric and structural features that play an important role in the field of image analysis and recognition. Contemporary extraction methods based on machine learning algorithms and fractal dimension are explained. The possibility of usage of these methods in real-life problems such as medical imaging, biometrics, remote sensing images processing and agriculture is considered. Successful implementation examples of information functions in real-life problems are presented and opportunities for further research on the topic are considered
New control strategy for maximizing power extraction in the grid-connected CHP-PV-Wind hybrid system
Get PDFThis work represents a significant contribution to the advancement of modern electrical systems by combining advanced control strategies with robust protection solutions to address the challenges of the energy transition. It focuses on the integration of renewable energy within electrical grids, with particular attention to wind energy, cogeneration (CHP), and photovoltaic energy. The main contributions include the development of innovative methods to enhance system stability and improve energy quality. This is achieved notably through the use of advanced control algorithms, such as the synchronously rotating frame (SRF) transformation, applied to converters and voltage source converter-based high voltage direct current (VSC-HVDC) systems. These approaches enable precise voltage regulation, optimized power flow management, and significant reduction of harmonic distortion. The paper also explores novel techniques, such as control based on the ANFIS algorithm, to improve voltage regulation, current stability, and converter efficiency. Finally, an effective protection solution against voltage faults is proposed, ensuring the stable and reliable transfer of energy produced by offshore wind farms to onshore grids
Facial emotion recognition under face mask occlusion using vision transformers
Get PDFFacial emotion recognition (FER) systems face significant challenges when individuals wear face masks, as critical facial regions are occluded. This paper addresses this limitation by employing vision transformers (ViT), which offer a promising alternative with reduced computational complexity compared to traditional deep learning methods. We propose a ViT-based FER framework that fine-tunes a pre-trained ViT architecture to enhance emotion recognition under mask-induced occlusion. The model is fine-tuned and evaluated on the AffectNet dataset, which originally represents eight emotion categories. These categories are restructured into five broader classes to mitigate the impact of occluded features. The modelβs performance is assessed using standard metrics, including accuracy, precision, recall, and F1 score. Experimental results demonstrate that the proposed framework achieves an accuracy of 81%, outperforming several state-of-the-art approaches. These findings highlight the potential of vision transformers in recognizing emotions under masked conditions and support the development of more robust FER systems for real-world applications in healthcare, surveillance, and humanβcomputer interaction. This work introduces a scalable and effective approach that integrates self-attention, synthetic mask augmentation, and emotion class restructuring to improve emotion recognition under facial occlusion
A systematic review of software fault prediction techniques: models, classifiers, and data processing approaches
Get PDFSoftware fault prediction (SFP) plays a critical role in improving software reliability by enabling early detection and correction of defects. This paper presents a comprehensive review of 25 recent and significant studies on SFP techniques, focusing on data preprocessing strategies, classification algorithms, and their effectiveness across various datasets. The review categorizes the approaches into traditional statistical models, machine learning methods, deep learning architectures, and hybrid techniques. Notably, wrapper-based feature selection, neural network classifiers, and support vector machines (SVM) are identified as the most effective in achieving high accuracy, particularly when dealing with imbalanced or noisy datasets. The paper also highlights advanced approaches such as variational autoencoders (VAE), Bayesian classifiers, and fuzzy clustering for fault prediction. Comparative analysis is provided to assess performance metrics such as accuracy, F-measure, and area under the curve (AUC). The findings suggest that no single method fits all scenarios, but a combination of appropriate preprocessing and robust classification yields optimal results. This review provides valuable insights for researchers and practitioners aiming to enhance software quality through predictive analytics. Future work should explore ensemble learning and real-time SFP systems for broader applicability
Application of the model reference adaptive system method in sensorless control for elevator drive systems using 3-Phase permanent magnet synchronous motors
Get PDFImproving sensorless control performance in elevator drive systems using three-phase permanent magnet synchronous motors (PMSM) has become increasingly popular to reduce costs and enhance system stability. The primary operation of the elevator involves motor mode when the cabin moves upward and shifts to generator mode or braking mode under the influence of gravity when moving downward. This presents significant challenges for sensorless control. To address these issues, the model reference adaptive system (MRAS) based on the mathematical d-q axis model of the PMSM is proposed to estimate rotor speed and position. Combined with field-oriented control (FOC), this method optimizes performance and precisely controls motor torque without requiring physical sensors. Additionally, a low-pass filter is employed to process input signals, such as voltage and current, to improve estimation accuracy and optimize speed response. Simulation results from MATLAB/Simulink demonstrate highly accurate speed responses, particularly under continuous load variations
A novel stretched-compressed exponential low-pass filter and its application to electrocardiogram signal denoising
Get PDFThe study investigates a novel stretched-compressed exponential low-pass (SCELP) filter to denoise electrocardiogram (ECG) signals. As an extension of Gaussian filter and unlike other denoising filters, the SCELP filter utilizes the stretched-compressed exponential function (SCEF) in the convolution kernel, being the Gaussian function its particular case. A MATLAB implementation is provided with a single parameter (Ξ²), which allows to modify the filter strength, to increase the signal-to-noise ratio (SNR) and reduce the mean squared error (MSE). The SCELP filterβs advantages over traditional denoising filters (i.e., Gaussian, MittagβLeffler, and Savitzky-Golay filters) were assessed on 100 ECG signals, 50 normal and 50 abnormal (affected by sleep apnea), provided by the PhysioNet dataset. The SCELP filterβs efficacy in rejecting noise was evaluated as the Ξ² parameter varies, quantifying the filters' performance in terms of mean SNR and MSE to determine the optimal Ξ² value. The obtained results showed that the SCELP filter's best performances are achieved for Ξ² equal to = 1.6 (i.e., 16.9508 dB and 13.7574 dB SNR values, and 0.01025 and 0.01178 MSE values for normal and abnormal ECGs, respectively). Furthermore, the SCELP filter was tested on ECG signals with added white noise; compared to Gaussian, MittagβLeffler, and Savitzky-Golay filters, the SCELP filter yields better performance regarding SNR (16.495 and 14.940 dB) and MSE (0.0106 and 0.0114) values, for normal and abnormal ECGs, respectively, suggesting its applicability for ECG signals' denoising
Detection of islanding using empirical mode decomposition and support vector machine
Get PDFAccurate detection of islanding remains to be a challenge for grid connected microgrid system. An effective method to identify the islanding of microgrid has been presented which uses only the voltage at point of common coupling (PCC). Accurate islanding detection is necessary to impose appropriate control for the microgrid operation. Following the islanding of microgrid the intrinsic mode functions (IMFβs) of voltage at PCC obtained by empirical mode decomposition (EMD) will be analyzed by support vector machine (SVM) model which identifies the islanding of the microgrid. SVM model learns through the training data set. As many as 150 simulated cases have been used to train the SVM. A practical microgrid system has been simulated for various operating conditions and the data generation has been carried out by series of simulations for various islanding and non-islanding events using MATLAB Simulink. The proposed method gives optimistic results with high accuracy, zero non detection zone (NDZ) and detection time as low as 63.11 ms. Accurate islanding detection leads to smooth transition of microgrid control essential for operators
Deep learning architecture for detection of fetal heart anomalies
Get PDFResearch has demonstrated that artificial intelligence (AI) techniques have shown tremendous potential over the past decade for analyzing and detecting anomalies in the fetal heart during ultrasound tests. Despite their potential, the adoption of these algorithms remains limited due to concerns over patient privacy, the scarcity of large well-annotated datasets and challenges in achieving high accuracy. This research aims to overcome these limitations by proposing an optimal solution. Two methods such as deterministic image augmentation techniques and Wasserstein generative adversarial network with gradient penalty (WGAN-GP) showcase the framework's capacity to seamlessly and effectively expand original datasets to 14 times and 17 times respectively, thereby effectively tackling the problem of data scarcity. It uses an annotation tool to precisely categorize anomalies identified in the echocardiogram dataset. Segmentation of the annotated data is done to highlight region of interest. Nine distinct fetal heart anomalies are identified with respect to the fewer covered in existing research. This study also investigates the state-of-the-art architectures and optimization techniques used in deep learning models. The results clearly indicate that the ResNet-101 model demonstrated superior precision accuracy of 99.15%. To ensure the reliability of the proposed model, its performance underwent thorough evaluation and validation by certified gynecologists and fetal medicine specialists